Deep Learning Based Continuous Real-Time Driver Fatigue Detection for Embedded System

2020 ◽  
Author(s):  
Esra Civik ◽  
Ugur Yuzgec
Keyword(s):  
Deep Learning ◽  
Embedded System ◽  
Real Time ◽  
Driver Fatigue ◽  
Fatigue Detection

Real-time driver fatigue detection based on morphology infrared features and deep learning

2018 ◽  
Vol 47 (2) ◽  
pp. 203009
Author(s):  
耿 磊 Geng Lei ◽  
梁晓昱 Liang Xiaoyu ◽  
肖志涛 Xiao Zhitao ◽  
李月龙 Li Yuelong
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Driver Fatigue ◽  
Fatigue Detection

scholarly journals A Real-time Driver Fatigue Detection Method Based on Two-Stage Convolutional Neural Network

2020 ◽  
Vol 53 (2) ◽  
pp. 15374-15379
Author(s):  
Hu He ◽  
Xiaoyong Zhang ◽  
Fu Jiang ◽  
Chenglong Wang ◽  
Yingze Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Detection Method ◽  
Driver Fatigue ◽  
Two Stage ◽  
Fatigue Detection

Real-Time Driver Fatigue Detection Based on ELM

2016 ◽  
pp. 423-435
Author(s):  
Hengyu Liu ◽  
Tiancheng Zhang ◽  
Haibin Xie ◽  
Hongbiao Chen ◽  
Fangfang Li
Keyword(s):  
Real Time ◽  
Driver Fatigue ◽  
Fatigue Detection

scholarly journals Deep Neural Network Based Ambient Airflow Control through Spatial Learning

Electronics ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 591
Author(s):  
Sunghak Kim ◽  
InChul Choi ◽  
Dohyeong Kim ◽  
Minho Lee
Keyword(s):  
Energy Efficiency ◽  
Deep Learning ◽  
Embedded System ◽  
Real Time ◽  
Learning Algorithm ◽  
Cost Effective ◽  
Target Temperature ◽  
Air Conditioner ◽  
Indoor Space ◽  
Living Area

As global energy regulations are strengthened, improving energy efficiency while maintaining performance of electronic appliances is becoming more important. Especially in air conditioning, energy efficiency can be maximized by adaptively controlling the airflow based on detected human locations; however, several limitations such as detection areas, the installation environment, and sensor quantity and real-time performance which come from the constraints in the embedded system make it a challenging problem. In this study, by using a low resolution cost effective vision sensor, the environmental information of living spaces and the real-time locations of humans are learned through a deep learning algorithm to identify the living area from the entire indoor space. Based on this information, we improve the performance and the energy efficiency of air conditioner by smartly controlling the airflow on the identified living area. In experiments, our deep learning based spatial classification algorithm shows error less than ± 5 ° . In addition, the target temperature can be reached 19.8% faster and the power consumption can be saved up to 20.5% by the time the target temperature is achieved.

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